data loading

data scaling

After checking for normalization, we scaled our data in the first place to provide the scaled data for further analysis.

3. Main questions

Question 1: Pedicted GI50-values

As we mentioned in our presentation, we want to create a model to predict GI50-values thus to predict, if Lapatinib is a good choice., The first linear model trys to predict the G-50 value under the data of the doubling time.

Fold_ChangeLap = select(Fold_Change, contains("Lapa"))
NegLogGI50Lap = NegLogGI50[9,]
means = colMeans(Fold_ChangeLap)
Fold_Changemeans = as.data.frame(t(means))

a2 = gsub(x = colnames (Fold_Changemeans), pattern = "_lapatinib_10000nM_24h", replacement = "")
colnames(Fold_Changemeans) = a2

a3 = gsub(x = a2, pattern = "X7", replacement = "7")
colnames(Fold_Changemeans) = a3


a1 = gsub(x = colnames (NegLogGI50Lap), pattern = "-", replacement = ".")
colnames(NegLogGI50Lap) = a1

c1 = rbind(a1,NegLogGI50Lap)
c2 = rbind(a3,Fold_Changemeans)

c1 = t(c1)
c2 = t(c2)

c1 =as.data.frame(c1)
c2 =as.data.frame(c2)


c3 = subset(c1, `1` %in% intersect(c1$`1`, c2$V1))
c4 = as.numeric(as.character(c3$lapatinib))
adjustedNeglogI50Lap = as.data.frame(c4)



Fold_Changemeans = as.data.frame(t(Fold_Changemeans))

combined1 = cbind(adjustedNeglogI50Lap, Fold_Changemeans)

names1 = c( "NegLogI50Lap","Fold_Changemeans")
colnames(combined1) = names1
                      
lmFold = lm(NegLogI50Lap ~ Fold_Changemeans, data = combined1)

summary(lmFold)
## 
## Call:
## lm(formula = NegLogI50Lap ~ Fold_Changemeans, data = combined1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0574 -0.4099 -0.1873  0.2076  2.0682 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      5.464e+00  9.539e-02  57.281   <2e-16 ***
## Fold_Changemeans 1.913e+15  7.519e+14   2.544    0.014 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6822 on 52 degrees of freedom
## Multiple R-squared:  0.1107, Adjusted R-squared:  0.09355 
## F-statistic:  6.47 on 1 and 52 DF,  p-value: 0.01398
qqnorm(lmFold$residuals, main = "Test for normaldistribution of residuals")
qqline(lmFold$residuals)

plot(combined1$NegLogI50Lap, lmFold$fitted.values, pch = 20, col = "blue", xlab = "Real values", 
     ylab = "Predicted values", main = "Comparison: real and predicted values ~ linear regression (Fold_Changemeans)")
abline(0, 1, col = "red")

cor(combined1$NegLogI50Lap,combined1$Fold_Changemeans)
## [1] 0.3326477
#Split the data (Training - Testing)

n = nrow(combined1)
rmse1 = sqrt(1/n * sum(lmFold$residuals^2))
rmse1
## [1] 0.6694461
i1.train = sample(1:nrow(combined1), 44)

dat1.train = combined1[i1.train, ]
dat1.test = combined1[-i1.train, ]

l1.train = lm(NegLogI50Lap ~ Fold_Changemeans, data = dat1.train)
summary(l1.train)
## 
## Call:
## lm(formula = NegLogI50Lap ~ Fold_Changemeans, data = dat1.train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0928 -0.4019 -0.2109  0.2030  2.0015 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      5.485e+00  1.082e-01  50.704   <2e-16 ***
## Fold_Changemeans 2.164e+15  9.354e+14   2.313   0.0257 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.699 on 42 degrees of freedom
## Multiple R-squared:  0.113,  Adjusted R-squared:  0.09187 
## F-statistic:  5.35 on 1 and 42 DF,  p-value: 0.02569
n = nrow(dat1.train)
rmse1.train = sqrt(1/n * sum(l1.train$residuals^2))
rmse1.train
## [1] 0.6828938
pred1 = predict(l1.train, newdata = dat1.test)

n = nrow(dat1.test)
residuals = dat1.test$NegLogI50Lap - pred1
rmse1.test1 = sqrt(1/n * sum(residuals^2))
rmse1.test1
## [1] 0.6145976

The second linear model trys to predict the G-50 value under the data of the Foldchange-means.

NegLogGI50Lap = NegLogGI50[9,]

#Sort by Cellline-Name
df = arrange(Cellline_Annotation, Cell_Line_Name)
Doublingtime = cbind.data.frame (df$Cell_Line_Name, df$Doubling_Time)

c21 = as.data.frame(t(NegLogGI50Lap))

combined2 = cbind(c21, Doublingtime$`df$Doubling_Time`)
names2 = c( "NegLogI50Lap","Doubling_Time")
colnames(combined2) = names2

combined2 =na.omit(combined2)

lmDouble = lm(NegLogI50Lap ~ Doubling_Time, data = combined2)

summary(lmDouble)
## 
## Call:
## lm(formula = NegLogI50Lap ~ Doubling_Time, data = combined2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2751 -0.4124 -0.1210  0.1709  2.0784 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   5.147415   0.245361  20.979   <2e-16 ***
## Doubling_Time 0.010536   0.006391   1.649    0.105    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6741 on 58 degrees of freedom
## Multiple R-squared:  0.04476,    Adjusted R-squared:  0.0283 
## F-statistic: 2.718 on 1 and 58 DF,  p-value: 0.1046
qqnorm(lmDouble$residuals, main = "Test for normaldistribution of residuals")
qqline(lmDouble$residuals)

plot(combined2$NegLogI50Lap, lmDouble$fitted.values, pch = 20, col = "blue", xlab = "Real values", 
     ylab = "Predicted values", main = "Comparison: real and predicted values ~ linear regression (Doubling-Time)")
abline(0, 1, col = "red")

cor(combined2$NegLogI50Lap,combined2$Doubling_Time)
## [1] 0.2115772
#Split the data (Training - Testing)

n = nrow(combined2)
rmse2 = sqrt(1/n * sum(lmDouble$residuals^2))
rmse2
## [1] 0.6627233
i2.train = sample(1:nrow(combined2), 48)

dat2.train = combined2[i2.train, ]
dat2.test = combined2[-i2.train, ]

l2.train = lm(NegLogI50Lap ~ Doubling_Time, data = dat2.train)
summary(l2.train)
## 
## Call:
## lm(formula = NegLogI50Lap ~ Doubling_Time, data = dat2.train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9740 -0.3577 -0.1473  0.1776  1.9844 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   5.010389   0.251749  19.902   <2e-16 ***
## Doubling_Time 0.014262   0.006528   2.185    0.034 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6345 on 46 degrees of freedom
## Multiple R-squared:  0.09402,    Adjusted R-squared:  0.07432 
## F-statistic: 4.774 on 1 and 46 DF,  p-value: 0.03403
n = nrow(dat2.train)
rmse2.train = sqrt(1/n * sum(l2.train$residuals^2))
rmse2.train
## [1] 0.6211052
pred2 = predict(l2.train, newdata = dat2.test)

n = nrow(dat1.test)
residuals = dat2.test$NegLogI50Lap - pred2
rmse2.test = sqrt(1/n * sum(residuals^2))
rmse2.test
## [1] 0.8938786

As a last part, we did a multiple regression with both datasets to predict GI50-values.

b1 = gsub(x =Doublingtime$`df$Cell_Line_Name`, pattern = "-", replacement = ".")
Doublingtime1 =  rbind(b1,Doublingtime$`df$Doubling_Time`)
Doublingtime1 = as.data.frame(t(Doublingtime1)) 

c31 = subset(Doublingtime1, b1 %in% intersect(Doublingtime1$b1, c2$V1))
c41 = as.numeric(as.character(c31$V2))
adjustedDoubling_Time = as.data.frame(c41)

combined3 = cbind(adjustedNeglogI50Lap, Fold_Changemeans, adjustedDoubling_Time)
names3 = c( "NegLogI50Lap","Fold_Changemeans","Doubling_Time")
colnames(combined3) = names3

mlr = lm(NegLogI50Lap ~ ., data = combined3)

summary(mlr)
## 
## Call:
## lm(formula = NegLogI50Lap ~ ., data = combined3)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3123 -0.3909 -0.1226  0.2003  1.8141 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      5.064e+00  2.655e-01  19.069   <2e-16 ***
## Fold_Changemeans 1.819e+15  7.429e+14   2.449   0.0178 *  
## Doubling_Time    1.083e-02  6.717e-03   1.612   0.1130    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6719 on 51 degrees of freedom
## Multiple R-squared:  0.1538, Adjusted R-squared:  0.1206 
## F-statistic: 4.635 on 2 and 51 DF,  p-value: 0.01415
qqnorm(mlr$residuals, main = "Test for normaldistribution of residuals")
qqline(mlr$residuals)

plot(combined3$NegLogI50Lap, mlr$fitted.values, pch = 20, col = "blue", xlab = "Real values", 
     ylab = "Predicted values" , main = "Comparison: real and predicted values ~ multiple regression")
abline(0, 1, col = "red")

#Split the data (Training - Testing)

n = nrow(combined3)
rmse3 = sqrt(1/n * sum(mlr$residuals^2))
rmse3
## [1] 0.6530072
i3.train = sample(1:nrow(combined2), 44)

dat3.train = combined3[i3.train, ]
dat3.test = combined3[-i3.train, ]

l3.train = lm(NegLogI50Lap ~ ., data = dat3.train)
summary(l3.train)
## 
## Call:
## lm(formula = NegLogI50Lap ~ ., data = dat3.train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4110 -0.4173 -0.1550  0.1519  1.7242 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      5.001e+00  3.341e-01  14.972   <2e-16 ***
## Fold_Changemeans 1.727e+15  1.003e+15   1.722   0.0935 .  
## Doubling_Time    1.355e-02  8.286e-03   1.636   0.1104    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.734 on 37 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.1461, Adjusted R-squared:  0.09993 
## F-statistic: 3.165 on 2 and 37 DF,  p-value: 0.05384
n = nrow(dat3.train)
rmse3.train = sqrt(1/n * sum(l3.train$residuals^2))
rmse3.train
## [1] 0.6730553
pred3 = predict(l3.train, newdata = dat3.test)

n = nrow(dat3.test)
residuals = dat3.test$NegLogI50Lap - pred3
rmse3.test = sqrt(1/n * sum(residuals^2))
rmse3.test
## [1] 0.4813735

As you can see from the data, All three regression models are not really good. ##Question 2: Erlotinib vs Lapatinib

# correlation in general 
n= as.data.frame(t(NegLogGI50))
rmv.rows = apply(n, 1, function(x) {
  sum(is.na(x))
})
NLGI50.all = n[-which(rmv.rows > 0), ]  # Removing any row with 1 or more missing values
rm(rmv.rows, n, NegLogGI50)
cor.mat = as.data.frame(cor(NLGI50.all[, 1:ncol(NLGI50.all)], method = "pearson")) #Pearson correlation
round(cor.mat, 2) #round values
##               5-Azacytidine bortezomib cisplatin dasatinib doxorubicin
## 5-Azacytidine          1.00      -0.08      0.16      0.18        0.29
## bortezomib            -0.08       1.00      0.01     -0.10        0.32
## cisplatin              0.16       0.01      1.00     -0.24        0.52
## dasatinib              0.18      -0.10     -0.24      1.00       -0.08
## doxorubicin            0.29       0.32      0.52     -0.08        1.00
## erlotinib              0.27      -0.32      0.01      0.42       -0.17
## geldanamycin           0.23       0.36      0.19     -0.09        0.23
## gemcitibine            0.16      -0.08      0.53     -0.03        0.37
## lapatinib              0.14      -0.26     -0.07      0.19       -0.16
## paclitaxel             0.10       0.20      0.01     -0.10        0.55
## sirolimus             -0.05       0.01      0.27      0.07        0.17
## sorafenib              0.09       0.27     -0.01     -0.24        0.14
## sunitinib              0.12      -0.01     -0.05     -0.03       -0.14
## topotecan              0.14       0.13      0.55      0.02        0.60
## vorinostat             0.16      -0.02      0.07     -0.16       -0.06
##               erlotinib geldanamycin gemcitibine lapatinib paclitaxel
## 5-Azacytidine      0.27         0.23        0.16      0.14       0.10
## bortezomib        -0.32         0.36       -0.08     -0.26       0.20
## cisplatin          0.01         0.19        0.53     -0.07       0.01
## dasatinib          0.42        -0.09       -0.03      0.19      -0.10
## doxorubicin       -0.17         0.23        0.37     -0.16       0.55
## erlotinib          1.00        -0.01        0.01      0.65      -0.37
## geldanamycin      -0.01         1.00        0.12     -0.01       0.28
## gemcitibine        0.01         0.12        1.00     -0.15       0.03
## lapatinib          0.65        -0.01       -0.15      1.00      -0.24
## paclitaxel        -0.37         0.28        0.03     -0.24       1.00
## sirolimus          0.21        -0.21        0.05      0.21      -0.04
## sorafenib         -0.29         0.14       -0.01     -0.25       0.29
## sunitinib          0.06         0.24        0.06      0.12      -0.02
## topotecan         -0.02         0.21        0.63     -0.14       0.20
## vorinostat         0.12         0.20        0.18      0.26       0.09
##               sirolimus sorafenib sunitinib topotecan vorinostat
## 5-Azacytidine     -0.05      0.09      0.12      0.14       0.16
## bortezomib         0.01      0.27     -0.01      0.13      -0.02
## cisplatin          0.27     -0.01     -0.05      0.55       0.07
## dasatinib          0.07     -0.24     -0.03      0.02      -0.16
## doxorubicin        0.17      0.14     -0.14      0.60      -0.06
## erlotinib          0.21     -0.29      0.06     -0.02       0.12
## geldanamycin      -0.21      0.14      0.24      0.21       0.20
## gemcitibine        0.05     -0.01      0.06      0.63       0.18
## lapatinib          0.21     -0.25      0.12     -0.14       0.26
## paclitaxel        -0.04      0.29     -0.02      0.20       0.09
## sirolimus          1.00     -0.11     -0.20      0.03       0.02
## sorafenib         -0.11      1.00      0.05      0.16       0.10
## sunitinib         -0.20      0.05      1.00     -0.05      -0.13
## topotecan          0.03      0.16     -0.05      1.00       0.08
## vorinostat         0.02      0.10     -0.13      0.08       1.00
pairs(NLGI50.all[, 1:ncol(NLGI50.all)], pch = 20, cex = 0.8, col = "royalblue3", main = "Correlation_NegLogGI50") 

plot erlotinib all genes, coloured by tissue

#differece
diff = data.frame(erlotinib = NLGI50.all$erlotinib - mean(NLGI50.all$erlotinib), lapatinib = NLGI50.all$lapatinib- mean(NLGI50.all$lapatinib))
diff$celllines = rownames(NLGI50.all)
#create vector to insert column tissue from Metadata

tissue = sapply(1:nrow(diff), function(x) {
  position = which(as.character(Metadata$cell) == diff[x, "celllines"])[1] #if tissue occurs several times, take the first
  out = as.character(Metadata[position, "tissue"]) #output the tissue at this position
  return(out)
})
diff$tissue = tissue
rm(tissue)

diff$celllines = factor(diff$celllines, levels = diff$celllines[order(diff$tissue)]) #Classified by tissue

ggplot(diff, aes(x = celllines, y = erlotinib, fill = tissue))+geom_bar(stat = "identity") + coord_flip() + labs(title = "Mean graph plot of NLGI50 values for Erlotinib")

The difference from the NegLogGI50 for a particular cell line and the mean NegLogGI50 is plotted here for Erlotinib.

plot lapatinib all genes, coloured by tissue

ggplot(diff, aes(x = celllines, y = lapatinib, fill = tissue)) + geom_bar(stat="identity") + coord_flip() + labs(title="Mean graph plot of NLGI50 values for Lapatinib")

The difference from the NegLogGI50 for a particular cell line and the mean NegLogGI50 is plotted here for Lapatinib.

correlation erlotinib , lapatinib

cor(NLGI50.all$erlotinib, NLGI50.all$lapatinib, method = "pearson")
## [1] 0.6528188

A Pearson correlation coefficient of ~ 0.65 confirms that these patterns are very similar

Lung genes

#only lung with mean all
### load data
Metadata_Lapatinib_treated = Metadata[which(Metadata$drug == "lapatinib" & Metadata$dose != "0nM"),]
NegLogGI50 = as.data.frame(readRDS(paste0(wd, "/Data/NegLogGI50.rds")))
#lung genes from Metadata
Lung_Metadata_L_treated = Metadata[which(Metadata$drug == "lapatinib" & Metadata$dose != "0nM" & Metadata$tissue == "Lung"),] 
celllines = Lung_Metadata_L_treated$cell
NegLogGI50.lung = as.data.frame(t(NegLogGI50[c("erlotinib", "lapatinib"), celllines]))

#Difference
dif.NegLogGI50.lung = data.frame(erlotinib = NegLogGI50.lung$erlotinib -  mean(NLGI50.all$erlotinib), lapatinib = NegLogGI50.lung$lapatinib -  mean(NLGI50.all$lapatinib)) #erlotinib data - mean value, lapatinib data - mean value
dif.NegLogGI50.lung$celllines = rownames(NegLogGI50.lung)

# PLot

ggplot(dif.NegLogGI50.lung,aes(x = celllines, y = erlotinib)) + geom_bar(stat = "identity", fill = "skyblue") + geom_text(aes(label = round(erlotinib, 2)), vjust = -0.5, color = "black", size = 3) + coord_flip() + labs(title = "Mean graph plot of NLGI50 values for Erlotinib, only Lung genes")

plot lapatinib

ggplot(dif.NegLogGI50.lung,aes(x = celllines, y = lapatinib)) + geom_bar(stat = "identity", fill = "skyblue") + geom_text(aes(label=round(lapatinib, 2)), vjust = -0.5, color = "black", size = 3) + coord_flip() + labs(title = "Mean graph plot of NLGI50 values for Lapatinib, only Lung genes")

correlation lung

cor(NegLogGI50.lung$erlotinib, NegLogGI50.lung$lapatinib, method = "pearson")
## [1] 0.9609488

A pearson correlation coefficent of ~ 0.96 suggests that Lapatinib has a similar effect on lung cancer as Erlotinib

anova

<<<<<<< HEAD

selection of Lapatinib and Erlotinib treated cells

lapa<-data.frame(Metadata[which(Metadata[,'drug'] == "lapatinib"), ])
erlo<-data.frame(Metadata[which(Metadata[,'drug'] == "erlotinib"), ])
el<-right_join(lapa,erlo, by="cell")
el
##                              sample.x        cell    drug.x  dose.x time.x
## 1         786-0_lapatinib_10000nM_24h       786-0 lapatinib 10000nM    24h
## 2             786-0_lapatinib_0nM_24h       786-0 lapatinib     0nM    24h
## 3          A498_lapatinib_10000nM_24h        A498 lapatinib 10000nM    24h
## 4              A498_lapatinib_0nM_24h        A498 lapatinib     0nM    24h
## 5          A549_lapatinib_10000nM_24h        A549 lapatinib 10000nM    24h
## 6              A549_lapatinib_0nM_24h        A549 lapatinib     0nM    24h
## 7          ACHN_lapatinib_10000nM_24h        ACHN lapatinib 10000nM    24h
## 8              ACHN_lapatinib_0nM_24h        ACHN lapatinib     0nM    24h
## 9        BT-549_lapatinib_10000nM_24h      BT-549 lapatinib 10000nM    24h
## 10           BT-549_lapatinib_0nM_24h      BT-549 lapatinib     0nM    24h
## 11       CAKI-1_lapatinib_10000nM_24h      CAKI-1 lapatinib 10000nM    24h
## 12           CAKI-1_lapatinib_0nM_24h      CAKI-1 lapatinib     0nM    24h
## 13                               <NA>    CCRF-CEM      <NA>    <NA>   <NA>
## 14       DU-145_lapatinib_10000nM_24h      DU-145 lapatinib 10000nM    24h
## 15           DU-145_lapatinib_0nM_24h      DU-145 lapatinib     0nM    24h
## 16         EKVX_lapatinib_10000nM_24h        EKVX lapatinib 10000nM    24h
## 17             EKVX_lapatinib_0nM_24h        EKVX lapatinib     0nM    24h
## 18     HCC-2998_lapatinib_10000nM_24h    HCC-2998 lapatinib 10000nM    24h
## 19         HCC-2998_lapatinib_0nM_24h    HCC-2998 lapatinib     0nM    24h
## 20      HCT-116_lapatinib_10000nM_24h     HCT-116 lapatinib 10000nM    24h
## 21          HCT-116_lapatinib_0nM_24h     HCT-116 lapatinib     0nM    24h
## 22       HCT-15_lapatinib_10000nM_24h      HCT-15 lapatinib 10000nM    24h
## 23           HCT-15_lapatinib_0nM_24h      HCT-15 lapatinib     0nM    24h
## 24                               <NA>       HL-60      <NA>    <NA>   <NA>
## 25       HOP-62_lapatinib_10000nM_24h      HOP-62 lapatinib 10000nM    24h
## 26           HOP-62_lapatinib_0nM_24h      HOP-62 lapatinib     0nM    24h
## 27       HOP-92_lapatinib_10000nM_24h      HOP-92 lapatinib 10000nM    24h
## 28           HOP-92_lapatinib_0nM_24h      HOP-92 lapatinib     0nM    24h
## 29      HS-578T_lapatinib_10000nM_24h     HS-578T lapatinib 10000nM    24h
## 30          HS-578T_lapatinib_0nM_24h     HS-578T lapatinib     0nM    24h
## 31                               <NA>        HT29      <NA>    <NA>   <NA>
## 32      IGR-OV1_lapatinib_10000nM_24h     IGR-OV1 lapatinib 10000nM    24h
## 33          IGR-OV1_lapatinib_0nM_24h     IGR-OV1 lapatinib     0nM    24h
## 34                               <NA>       K-562      <NA>    <NA>   <NA>
## 35         KM12_lapatinib_10000nM_24h        KM12 lapatinib 10000nM    24h
## 36             KM12_lapatinib_0nM_24h        KM12 lapatinib     0nM    24h
## 37                               <NA>         LOX      <NA>    <NA>   <NA>
## 38          M14_lapatinib_10000nM_24h         M14 lapatinib 10000nM    24h
## 39              M14_lapatinib_0nM_24h         M14 lapatinib     0nM    24h
## 40     MALME-3M_lapatinib_10000nM_24h    MALME-3M lapatinib 10000nM    24h
## 41         MALME-3M_lapatinib_0nM_24h    MALME-3M lapatinib     0nM    24h
## 42         MCF7_lapatinib_10000nM_24h        MCF7 lapatinib 10000nM    24h
## 43             MCF7_lapatinib_0nM_24h        MCF7 lapatinib     0nM    24h
## 44   MDA-MB-231_lapatinib_10000nM_24h  MDA-MB-231 lapatinib 10000nM    24h
## 45       MDA-MB-231_lapatinib_0nM_24h  MDA-MB-231 lapatinib     0nM    24h
## 46   MDA-MB-435_lapatinib_10000nM_24h  MDA-MB-435 lapatinib 10000nM    24h
## 47       MDA-MB-435_lapatinib_0nM_24h  MDA-MB-435 lapatinib     0nM    24h
## 48   MDA-MB-468_lapatinib_10000nM_24h  MDA-MB-468 lapatinib 10000nM    24h
## 49       MDA-MB-468_lapatinib_0nM_24h  MDA-MB-468 lapatinib     0nM    24h
## 50       MOLT-4_lapatinib_10000nM_24h      MOLT-4 lapatinib 10000nM    24h
## 51           MOLT-4_lapatinib_0nM_24h      MOLT-4 lapatinib     0nM    24h
## 52  NCI-ADR-RES_lapatinib_10000nM_24h NCI-ADR-RES lapatinib 10000nM    24h
## 53      NCI-ADR-RES_lapatinib_0nM_24h NCI-ADR-RES lapatinib     0nM    24h
## 54     NCI-H226_lapatinib_10000nM_24h    NCI-H226 lapatinib 10000nM    24h
## 55         NCI-H226_lapatinib_0nM_24h    NCI-H226 lapatinib     0nM    24h
## 56      NCI-H23_lapatinib_10000nM_24h     NCI-H23 lapatinib 10000nM    24h
## 57          NCI-H23_lapatinib_0nM_24h     NCI-H23 lapatinib     0nM    24h
## 58    NCI-H322M_lapatinib_10000nM_24h   NCI-H322M lapatinib 10000nM    24h
## 59        NCI-H322M_lapatinib_0nM_24h   NCI-H322M lapatinib     0nM    24h
## 60     NCI-H460_lapatinib_10000nM_24h    NCI-H460 lapatinib 10000nM    24h
## 61         NCI-H460_lapatinib_0nM_24h    NCI-H460 lapatinib     0nM    24h
## 62     NCI-H522_lapatinib_10000nM_24h    NCI-H522 lapatinib 10000nM    24h
## 63         NCI-H522_lapatinib_0nM_24h    NCI-H522 lapatinib     0nM    24h
## 64      OVCAR-3_lapatinib_10000nM_24h     OVCAR-3 lapatinib 10000nM    24h
## 65          OVCAR-3_lapatinib_0nM_24h     OVCAR-3 lapatinib     0nM    24h
## 66      OVCAR-4_lapatinib_10000nM_24h     OVCAR-4 lapatinib 10000nM    24h
## 67          OVCAR-4_lapatinib_0nM_24h     OVCAR-4 lapatinib     0nM    24h
## 68      OVCAR-5_lapatinib_10000nM_24h     OVCAR-5 lapatinib 10000nM    24h
## 69          OVCAR-5_lapatinib_0nM_24h     OVCAR-5 lapatinib     0nM    24h
## 70      OVCAR-8_lapatinib_10000nM_24h     OVCAR-8 lapatinib 10000nM    24h
## 71          OVCAR-8_lapatinib_0nM_24h     OVCAR-8 lapatinib     0nM    24h
## 72         PC-3_lapatinib_10000nM_24h        PC-3 lapatinib 10000nM    24h
## 73             PC-3_lapatinib_0nM_24h        PC-3 lapatinib     0nM    24h
## 74    RPMI-8226_lapatinib_10000nM_24h   RPMI-8226 lapatinib 10000nM    24h
## 75        RPMI-8226_lapatinib_0nM_24h   RPMI-8226 lapatinib     0nM    24h
## 76      RXF-393_lapatinib_10000nM_24h     RXF-393 lapatinib 10000nM    24h
## 77          RXF-393_lapatinib_0nM_24h     RXF-393 lapatinib     0nM    24h
## 78       SF-268_lapatinib_10000nM_24h      SF-268 lapatinib 10000nM    24h
## 79           SF-268_lapatinib_0nM_24h      SF-268 lapatinib     0nM    24h
## 80       SF-295_lapatinib_10000nM_24h      SF-295 lapatinib 10000nM    24h
## 81           SF-295_lapatinib_0nM_24h      SF-295 lapatinib     0nM    24h
## 82       SF-539_lapatinib_10000nM_24h      SF-539 lapatinib 10000nM    24h
## 83           SF-539_lapatinib_0nM_24h      SF-539 lapatinib     0nM    24h
## 84     SK-MEL-2_lapatinib_10000nM_24h    SK-MEL-2 lapatinib 10000nM    24h
## 85         SK-MEL-2_lapatinib_0nM_24h    SK-MEL-2 lapatinib     0nM    24h
## 86    SK-MEL-28_lapatinib_10000nM_24h   SK-MEL-28 lapatinib 10000nM    24h
## 87        SK-MEL-28_lapatinib_0nM_24h   SK-MEL-28 lapatinib     0nM    24h
## 88     SK-MEL-5_lapatinib_10000nM_24h    SK-MEL-5 lapatinib 10000nM    24h
## 89         SK-MEL-5_lapatinib_0nM_24h    SK-MEL-5 lapatinib     0nM    24h
## 90      SK-OV-3_lapatinib_10000nM_24h     SK-OV-3 lapatinib 10000nM    24h
## 91          SK-OV-3_lapatinib_0nM_24h     SK-OV-3 lapatinib     0nM    24h
## 92        SN12C_lapatinib_10000nM_24h       SN12C lapatinib 10000nM    24h
## 93            SN12C_lapatinib_0nM_24h       SN12C lapatinib     0nM    24h
## 94       SNB-19_lapatinib_10000nM_24h      SNB-19 lapatinib 10000nM    24h
## 95           SNB-19_lapatinib_0nM_24h      SNB-19 lapatinib     0nM    24h
## 96       SNB-75_lapatinib_10000nM_24h      SNB-75 lapatinib 10000nM    24h
## 97           SNB-75_lapatinib_0nM_24h      SNB-75 lapatinib     0nM    24h
## 98                               <NA>          SR      <NA>    <NA>   <NA>
## 99       SW-620_lapatinib_10000nM_24h      SW-620 lapatinib 10000nM    24h
## 100          SW-620_lapatinib_0nM_24h      SW-620 lapatinib     0nM    24h
## 101       T-47D_lapatinib_10000nM_24h       T-47D lapatinib 10000nM    24h
## 102           T-47D_lapatinib_0nM_24h       T-47D lapatinib     0nM    24h
## 103       TK-10_lapatinib_10000nM_24h       TK-10 lapatinib 10000nM    24h
## 104           TK-10_lapatinib_0nM_24h       TK-10 lapatinib     0nM    24h
## 105        U251_lapatinib_10000nM_24h        U251 lapatinib 10000nM    24h
## 106            U251_lapatinib_0nM_24h        U251 lapatinib     0nM    24h
## 107    UACC-257_lapatinib_10000nM_24h    UACC-257 lapatinib 10000nM    24h
## 108        UACC-257_lapatinib_0nM_24h    UACC-257 lapatinib     0nM    24h
## 109     UACC-62_lapatinib_10000nM_24h     UACC-62 lapatinib 10000nM    24h
## 110         UACC-62_lapatinib_0nM_24h     UACC-62 lapatinib     0nM    24h
## 111       UO-31_lapatinib_10000nM_24h       UO-31 lapatinib 10000nM    24h
## 112           UO-31_lapatinib_0nM_24h       UO-31 lapatinib     0nM    24h
## 113       786-0_lapatinib_10000nM_24h       786-0 lapatinib 10000nM    24h
## 114           786-0_lapatinib_0nM_24h       786-0 lapatinib     0nM    24h
## 115        A498_lapatinib_10000nM_24h        A498 lapatinib 10000nM    24h
## 116            A498_lapatinib_0nM_24h        A498 lapatinib     0nM    24h
## 117        A549_lapatinib_10000nM_24h        A549 lapatinib 10000nM    24h
## 118            A549_lapatinib_0nM_24h        A549 lapatinib     0nM    24h
## 119        ACHN_lapatinib_10000nM_24h        ACHN lapatinib 10000nM    24h
## 120            ACHN_lapatinib_0nM_24h        ACHN lapatinib     0nM    24h
## 121      BT-549_lapatinib_10000nM_24h      BT-549 lapatinib 10000nM    24h
## 122          BT-549_lapatinib_0nM_24h      BT-549 lapatinib     0nM    24h
## 123      CAKI-1_lapatinib_10000nM_24h      CAKI-1 lapatinib 10000nM    24h
## 124          CAKI-1_lapatinib_0nM_24h      CAKI-1 lapatinib     0nM    24h
## 125                              <NA>    CCRF-CEM      <NA>    <NA>   <NA>
## 126      DU-145_lapatinib_10000nM_24h      DU-145 lapatinib 10000nM    24h
## 127          DU-145_lapatinib_0nM_24h      DU-145 lapatinib     0nM    24h
## 128        EKVX_lapatinib_10000nM_24h        EKVX lapatinib 10000nM    24h
## 129            EKVX_lapatinib_0nM_24h        EKVX lapatinib     0nM    24h
## 130    HCC-2998_lapatinib_10000nM_24h    HCC-2998 lapatinib 10000nM    24h
## 131        HCC-2998_lapatinib_0nM_24h    HCC-2998 lapatinib     0nM    24h
## 132     HCT-116_lapatinib_10000nM_24h     HCT-116 lapatinib 10000nM    24h
## 133         HCT-116_lapatinib_0nM_24h     HCT-116 lapatinib     0nM    24h
## 134      HCT-15_lapatinib_10000nM_24h      HCT-15 lapatinib 10000nM    24h
## 135          HCT-15_lapatinib_0nM_24h      HCT-15 lapatinib     0nM    24h
## 136                              <NA>       HL-60      <NA>    <NA>   <NA>
## 137      HOP-62_lapatinib_10000nM_24h      HOP-62 lapatinib 10000nM    24h
## 138          HOP-62_lapatinib_0nM_24h      HOP-62 lapatinib     0nM    24h
## 139      HOP-92_lapatinib_10000nM_24h      HOP-92 lapatinib 10000nM    24h
## 140          HOP-92_lapatinib_0nM_24h      HOP-92 lapatinib     0nM    24h
## 141     HS-578T_lapatinib_10000nM_24h     HS-578T lapatinib 10000nM    24h
## 142         HS-578T_lapatinib_0nM_24h     HS-578T lapatinib     0nM    24h
## 143                              <NA>        HT29      <NA>    <NA>   <NA>
## 144     IGR-OV1_lapatinib_10000nM_24h     IGR-OV1 lapatinib 10000nM    24h
## 145         IGR-OV1_lapatinib_0nM_24h     IGR-OV1 lapatinib     0nM    24h
## 146                              <NA>       K-562      <NA>    <NA>   <NA>
## 147        KM12_lapatinib_10000nM_24h        KM12 lapatinib 10000nM    24h
## 148            KM12_lapatinib_0nM_24h        KM12 lapatinib     0nM    24h
## 149                              <NA>         LOX      <NA>    <NA>   <NA>
## 150         M14_lapatinib_10000nM_24h         M14 lapatinib 10000nM    24h
## 151             M14_lapatinib_0nM_24h         M14 lapatinib     0nM    24h
## 152    MALME-3M_lapatinib_10000nM_24h    MALME-3M lapatinib 10000nM    24h
## 153        MALME-3M_lapatinib_0nM_24h    MALME-3M lapatinib     0nM    24h
## 154        MCF7_lapatinib_10000nM_24h        MCF7 lapatinib 10000nM    24h
## 155            MCF7_lapatinib_0nM_24h        MCF7 lapatinib     0nM    24h
## 156  MDA-MB-231_lapatinib_10000nM_24h  MDA-MB-231 lapatinib 10000nM    24h
## 157      MDA-MB-231_lapatinib_0nM_24h  MDA-MB-231 lapatinib     0nM    24h
## 158  MDA-MB-435_lapatinib_10000nM_24h  MDA-MB-435 lapatinib 10000nM    24h
## 159      MDA-MB-435_lapatinib_0nM_24h  MDA-MB-435 lapatinib     0nM    24h
## 160  MDA-MB-468_lapatinib_10000nM_24h  MDA-MB-468 lapatinib 10000nM    24h
## 161      MDA-MB-468_lapatinib_0nM_24h  MDA-MB-468 lapatinib     0nM    24h
## 162      MOLT-4_lapatinib_10000nM_24h      MOLT-4 lapatinib 10000nM    24h
## 163          MOLT-4_lapatinib_0nM_24h      MOLT-4 lapatinib     0nM    24h
## 164 NCI-ADR-RES_lapatinib_10000nM_24h NCI-ADR-RES lapatinib 10000nM    24h
## 165     NCI-ADR-RES_lapatinib_0nM_24h NCI-ADR-RES lapatinib     0nM    24h
## 166    NCI-H226_lapatinib_10000nM_24h    NCI-H226 lapatinib 10000nM    24h
## 167        NCI-H226_lapatinib_0nM_24h    NCI-H226 lapatinib     0nM    24h
## 168     NCI-H23_lapatinib_10000nM_24h     NCI-H23 lapatinib 10000nM    24h
## 169         NCI-H23_lapatinib_0nM_24h     NCI-H23 lapatinib     0nM    24h
## 170   NCI-H322M_lapatinib_10000nM_24h   NCI-H322M lapatinib 10000nM    24h
## 171       NCI-H322M_lapatinib_0nM_24h   NCI-H322M lapatinib     0nM    24h
## 172    NCI-H460_lapatinib_10000nM_24h    NCI-H460 lapatinib 10000nM    24h
## 173        NCI-H460_lapatinib_0nM_24h    NCI-H460 lapatinib     0nM    24h
## 174    NCI-H522_lapatinib_10000nM_24h    NCI-H522 lapatinib 10000nM    24h
## 175        NCI-H522_lapatinib_0nM_24h    NCI-H522 lapatinib     0nM    24h
## 176     OVCAR-3_lapatinib_10000nM_24h     OVCAR-3 lapatinib 10000nM    24h
## 177         OVCAR-3_lapatinib_0nM_24h     OVCAR-3 lapatinib     0nM    24h
## 178     OVCAR-4_lapatinib_10000nM_24h     OVCAR-4 lapatinib 10000nM    24h
## 179         OVCAR-4_lapatinib_0nM_24h     OVCAR-4 lapatinib     0nM    24h
## 180     OVCAR-5_lapatinib_10000nM_24h     OVCAR-5 lapatinib 10000nM    24h
## 181         OVCAR-5_lapatinib_0nM_24h     OVCAR-5 lapatinib     0nM    24h
## 182     OVCAR-8_lapatinib_10000nM_24h     OVCAR-8 lapatinib 10000nM    24h
## 183         OVCAR-8_lapatinib_0nM_24h     OVCAR-8 lapatinib     0nM    24h
## 184        PC-3_lapatinib_10000nM_24h        PC-3 lapatinib 10000nM    24h
## 185            PC-3_lapatinib_0nM_24h        PC-3 lapatinib     0nM    24h
## 186   RPMI-8226_lapatinib_10000nM_24h   RPMI-8226 lapatinib 10000nM    24h
## 187       RPMI-8226_lapatinib_0nM_24h   RPMI-8226 lapatinib     0nM    24h
## 188     RXF-393_lapatinib_10000nM_24h     RXF-393 lapatinib 10000nM    24h
## 189         RXF-393_lapatinib_0nM_24h     RXF-393 lapatinib     0nM    24h
## 190      SF-268_lapatinib_10000nM_24h      SF-268 lapatinib 10000nM    24h
## 191          SF-268_lapatinib_0nM_24h      SF-268 lapatinib     0nM    24h
## 192      SF-295_lapatinib_10000nM_24h      SF-295 lapatinib 10000nM    24h
## 193          SF-295_lapatinib_0nM_24h      SF-295 lapatinib     0nM    24h
## 194      SF-539_lapatinib_10000nM_24h      SF-539 lapatinib 10000nM    24h
## 195          SF-539_lapatinib_0nM_24h      SF-539 lapatinib     0nM    24h
## 196    SK-MEL-2_lapatinib_10000nM_24h    SK-MEL-2 lapatinib 10000nM    24h
## 197        SK-MEL-2_lapatinib_0nM_24h    SK-MEL-2 lapatinib     0nM    24h
## 198   SK-MEL-28_lapatinib_10000nM_24h   SK-MEL-28 lapatinib 10000nM    24h
## 199       SK-MEL-28_lapatinib_0nM_24h   SK-MEL-28 lapatinib     0nM    24h
## 200    SK-MEL-5_lapatinib_10000nM_24h    SK-MEL-5 lapatinib 10000nM    24h
## 201        SK-MEL-5_lapatinib_0nM_24h    SK-MEL-5 lapatinib     0nM    24h
## 202     SK-OV-3_lapatinib_10000nM_24h     SK-OV-3 lapatinib 10000nM    24h
## 203         SK-OV-3_lapatinib_0nM_24h     SK-OV-3 lapatinib     0nM    24h
## 204       SN12C_lapatinib_10000nM_24h       SN12C lapatinib 10000nM    24h
## 205           SN12C_lapatinib_0nM_24h       SN12C lapatinib     0nM    24h
## 206      SNB-19_lapatinib_10000nM_24h      SNB-19 lapatinib 10000nM    24h
## 207          SNB-19_lapatinib_0nM_24h      SNB-19 lapatinib     0nM    24h
## 208      SNB-75_lapatinib_10000nM_24h      SNB-75 lapatinib 10000nM    24h
## 209          SNB-75_lapatinib_0nM_24h      SNB-75 lapatinib     0nM    24h
## 210                              <NA>          SR      <NA>    <NA>   <NA>
## 211      SW-620_lapatinib_10000nM_24h      SW-620 lapatinib 10000nM    24h
## 212          SW-620_lapatinib_0nM_24h      SW-620 lapatinib     0nM    24h
## 213       T-47D_lapatinib_10000nM_24h       T-47D lapatinib 10000nM    24h
## 214           T-47D_lapatinib_0nM_24h       T-47D lapatinib     0nM    24h
## 215       TK-10_lapatinib_10000nM_24h       TK-10 lapatinib 10000nM    24h
## 216           TK-10_lapatinib_0nM_24h       TK-10 lapatinib     0nM    24h
## 217        U251_lapatinib_10000nM_24h        U251 lapatinib 10000nM    24h
## 218            U251_lapatinib_0nM_24h        U251 lapatinib     0nM    24h
## 219    UACC-257_lapatinib_10000nM_24h    UACC-257 lapatinib 10000nM    24h
## 220        UACC-257_lapatinib_0nM_24h    UACC-257 lapatinib     0nM    24h
## 221     UACC-62_lapatinib_10000nM_24h     UACC-62 lapatinib 10000nM    24h
## 222         UACC-62_lapatinib_0nM_24h     UACC-62 lapatinib     0nM    24h
## 223       UO-31_lapatinib_10000nM_24h       UO-31 lapatinib 10000nM    24h
## 224           UO-31_lapatinib_0nM_24h       UO-31 lapatinib     0nM    24h
##     tissue.x                          sample.y    drug.y  dose.y time.y
## 1      Renal       786-0_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 2      Renal       786-0_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 3      Renal        A498_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 4      Renal        A498_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 5       Lung        A549_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 6       Lung        A549_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 7      Renal        ACHN_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 8      Renal        ACHN_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 9     Breast      BT-549_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 10    Breast      BT-549_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 11     Renal      CAKI-1_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 12     Renal      CAKI-1_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 13      <NA>    CCRF-CEM_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 14  Prostate      DU-145_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 15  Prostate      DU-145_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 16      Lung        EKVX_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 17      Lung        EKVX_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 18     Colon    HCC-2998_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 19     Colon    HCC-2998_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 20     Colon     HCT-116_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 21     Colon     HCT-116_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 22     Colon      HCT-15_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 23     Colon      HCT-15_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 24      <NA>       HL-60_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 25      Lung      HOP-62_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 26      Lung      HOP-62_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 27      Lung      HOP-92_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 28      Lung      HOP-92_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 29    Breast     HS-578T_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 30    Breast     HS-578T_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 31      <NA>        HT29_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 32   Ovarian     IGR-OV1_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 33   Ovarian     IGR-OV1_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 34      <NA>       K-562_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 35     Colon        KM12_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 36     Colon        KM12_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 37      <NA>         LOX_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 38  Melanoma         M14_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 39  Melanoma         M14_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 40  Melanoma    MALME-3M_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 41  Melanoma    MALME-3M_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 42    Breast        MCF7_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 43    Breast        MCF7_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 44    Breast  MDA-MB-231_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 45    Breast  MDA-MB-231_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 46  Melanoma  MDA-MB-435_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 47  Melanoma  MDA-MB-435_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 48    Breast  MDA-MB-468_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 49    Breast  MDA-MB-468_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 50  Leukemia      MOLT-4_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 51  Leukemia      MOLT-4_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 52   Ovarian NCI-ADR-RES_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 53   Ovarian NCI-ADR-RES_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 54      Lung    NCI-H226_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 55      Lung    NCI-H226_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 56      Lung     NCI-H23_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 57      Lung     NCI-H23_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 58      Lung   NCI-H322M_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 59      Lung   NCI-H322M_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 60      Lung    NCI-H460_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 61      Lung    NCI-H460_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 62      Lung    NCI-H522_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 63      Lung    NCI-H522_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 64   Ovarian     OVCAR-3_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 65   Ovarian     OVCAR-3_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 66   Ovarian     OVCAR-4_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 67   Ovarian     OVCAR-4_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 68   Ovarian     OVCAR-5_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 69   Ovarian     OVCAR-5_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 70   Ovarian     OVCAR-8_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 71   Ovarian     OVCAR-8_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 72  Prostate        PC-3_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 73  Prostate        PC-3_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 74  Leukemia   RPMI-8226_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 75  Leukemia   RPMI-8226_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 76     Renal     RXF-393_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 77     Renal     RXF-393_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 78       CNS      SF-268_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 79       CNS      SF-268_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 80       CNS      SF-295_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 81       CNS      SF-295_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 82       CNS      SF-539_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 83       CNS      SF-539_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 84  Melanoma    SK-MEL-2_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 85  Melanoma    SK-MEL-2_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 86  Melanoma   SK-MEL-28_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 87  Melanoma   SK-MEL-28_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 88  Melanoma    SK-MEL-5_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 89  Melanoma    SK-MEL-5_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 90   Ovarian     SK-OV-3_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 91   Ovarian     SK-OV-3_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 92     Renal       SN12C_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 93     Renal       SN12C_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 94       CNS      SNB-19_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 95       CNS      SNB-19_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 96       CNS      SNB-75_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 97       CNS      SNB-75_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 98      <NA>          SR_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 99     Colon      SW-620_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 100    Colon      SW-620_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 101   Breast       T-47D_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 102   Breast       T-47D_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 103    Renal       TK-10_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 104    Renal       TK-10_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 105      CNS        U251_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 106      CNS        U251_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 107 Melanoma    UACC-257_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 108 Melanoma    UACC-257_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 109 Melanoma     UACC-62_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 110 Melanoma     UACC-62_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 111    Renal       UO-31_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 112    Renal       UO-31_erlotinib_10000nM_24h erlotinib 10000nM    24h
## 113    Renal           786-0_erlotinib_0nM_24h erlotinib     0nM    24h
## 114    Renal           786-0_erlotinib_0nM_24h erlotinib     0nM    24h
## 115    Renal            A498_erlotinib_0nM_24h erlotinib     0nM    24h
## 116    Renal            A498_erlotinib_0nM_24h erlotinib     0nM    24h
## 117     Lung            A549_erlotinib_0nM_24h erlotinib     0nM    24h
## 118     Lung            A549_erlotinib_0nM_24h erlotinib     0nM    24h
## 119    Renal            ACHN_erlotinib_0nM_24h erlotinib     0nM    24h
## 120    Renal            ACHN_erlotinib_0nM_24h erlotinib     0nM    24h
## 121   Breast          BT-549_erlotinib_0nM_24h erlotinib     0nM    24h
## 122   Breast          BT-549_erlotinib_0nM_24h erlotinib     0nM    24h
## 123    Renal          CAKI-1_erlotinib_0nM_24h erlotinib     0nM    24h
## 124    Renal          CAKI-1_erlotinib_0nM_24h erlotinib     0nM    24h
## 125     <NA>        CCRF-CEM_erlotinib_0nM_24h erlotinib     0nM    24h
## 126 Prostate          DU-145_erlotinib_0nM_24h erlotinib     0nM    24h
## 127 Prostate          DU-145_erlotinib_0nM_24h erlotinib     0nM    24h
## 128     Lung            EKVX_erlotinib_0nM_24h erlotinib     0nM    24h
## 129     Lung            EKVX_erlotinib_0nM_24h erlotinib     0nM    24h
## 130    Colon        HCC-2998_erlotinib_0nM_24h erlotinib     0nM    24h
## 131    Colon        HCC-2998_erlotinib_0nM_24h erlotinib     0nM    24h
## 132    Colon         HCT-116_erlotinib_0nM_24h erlotinib     0nM    24h
## 133    Colon         HCT-116_erlotinib_0nM_24h erlotinib     0nM    24h
## 134    Colon          HCT-15_erlotinib_0nM_24h erlotinib     0nM    24h
## 135    Colon          HCT-15_erlotinib_0nM_24h erlotinib     0nM    24h
## 136     <NA>           HL-60_erlotinib_0nM_24h erlotinib     0nM    24h
## 137     Lung          HOP-62_erlotinib_0nM_24h erlotinib     0nM    24h
## 138     Lung          HOP-62_erlotinib_0nM_24h erlotinib     0nM    24h
## 139     Lung          HOP-92_erlotinib_0nM_24h erlotinib     0nM    24h
## 140     Lung          HOP-92_erlotinib_0nM_24h erlotinib     0nM    24h
## 141   Breast         HS-578T_erlotinib_0nM_24h erlotinib     0nM    24h
## 142   Breast         HS-578T_erlotinib_0nM_24h erlotinib     0nM    24h
## 143     <NA>            HT29_erlotinib_0nM_24h erlotinib     0nM    24h
## 144  Ovarian         IGR-OV1_erlotinib_0nM_24h erlotinib     0nM    24h
## 145  Ovarian         IGR-OV1_erlotinib_0nM_24h erlotinib     0nM    24h
## 146     <NA>           K-562_erlotinib_0nM_24h erlotinib     0nM    24h
## 147    Colon            KM12_erlotinib_0nM_24h erlotinib     0nM    24h
## 148    Colon            KM12_erlotinib_0nM_24h erlotinib     0nM    24h
## 149     <NA>             LOX_erlotinib_0nM_24h erlotinib     0nM    24h
## 150 Melanoma             M14_erlotinib_0nM_24h erlotinib     0nM    24h
## 151 Melanoma             M14_erlotinib_0nM_24h erlotinib     0nM    24h
## 152 Melanoma        MALME-3M_erlotinib_0nM_24h erlotinib     0nM    24h
## 153 Melanoma        MALME-3M_erlotinib_0nM_24h erlotinib     0nM    24h
## 154   Breast            MCF7_erlotinib_0nM_24h erlotinib     0nM    24h
## 155   Breast            MCF7_erlotinib_0nM_24h erlotinib     0nM    24h
## 156   Breast      MDA-MB-231_erlotinib_0nM_24h erlotinib     0nM    24h
## 157   Breast      MDA-MB-231_erlotinib_0nM_24h erlotinib     0nM    24h
## 158 Melanoma      MDA-MB-435_erlotinib_0nM_24h erlotinib     0nM    24h
## 159 Melanoma      MDA-MB-435_erlotinib_0nM_24h erlotinib     0nM    24h
## 160   Breast      MDA-MB-468_erlotinib_0nM_24h erlotinib     0nM    24h
## 161   Breast      MDA-MB-468_erlotinib_0nM_24h erlotinib     0nM    24h
## 162 Leukemia          MOLT-4_erlotinib_0nM_24h erlotinib     0nM    24h
## 163 Leukemia          MOLT-4_erlotinib_0nM_24h erlotinib     0nM    24h
## 164  Ovarian     NCI-ADR-RES_erlotinib_0nM_24h erlotinib     0nM    24h
## 165  Ovarian     NCI-ADR-RES_erlotinib_0nM_24h erlotinib     0nM    24h
## 166     Lung        NCI-H226_erlotinib_0nM_24h erlotinib     0nM    24h
## 167     Lung        NCI-H226_erlotinib_0nM_24h erlotinib     0nM    24h
## 168     Lung         NCI-H23_erlotinib_0nM_24h erlotinib     0nM    24h
## 169     Lung         NCI-H23_erlotinib_0nM_24h erlotinib     0nM    24h
## 170     Lung       NCI-H322M_erlotinib_0nM_24h erlotinib     0nM    24h
## 171     Lung       NCI-H322M_erlotinib_0nM_24h erlotinib     0nM    24h
## 172     Lung        NCI-H460_erlotinib_0nM_24h erlotinib     0nM    24h
## 173     Lung        NCI-H460_erlotinib_0nM_24h erlotinib     0nM    24h
## 174     Lung        NCI-H522_erlotinib_0nM_24h erlotinib     0nM    24h
## 175     Lung        NCI-H522_erlotinib_0nM_24h erlotinib     0nM    24h
## 176  Ovarian         OVCAR-3_erlotinib_0nM_24h erlotinib     0nM    24h
## 177  Ovarian         OVCAR-3_erlotinib_0nM_24h erlotinib     0nM    24h
## 178  Ovarian         OVCAR-4_erlotinib_0nM_24h erlotinib     0nM    24h
## 179  Ovarian         OVCAR-4_erlotinib_0nM_24h erlotinib     0nM    24h
## 180  Ovarian         OVCAR-5_erlotinib_0nM_24h erlotinib     0nM    24h
## 181  Ovarian         OVCAR-5_erlotinib_0nM_24h erlotinib     0nM    24h
## 182  Ovarian         OVCAR-8_erlotinib_0nM_24h erlotinib     0nM    24h
## 183  Ovarian         OVCAR-8_erlotinib_0nM_24h erlotinib     0nM    24h
## 184 Prostate            PC-3_erlotinib_0nM_24h erlotinib     0nM    24h
## 185 Prostate            PC-3_erlotinib_0nM_24h erlotinib     0nM    24h
## 186 Leukemia       RPMI-8226_erlotinib_0nM_24h erlotinib     0nM    24h
## 187 Leukemia       RPMI-8226_erlotinib_0nM_24h erlotinib     0nM    24h
## 188    Renal         RXF-393_erlotinib_0nM_24h erlotinib     0nM    24h
## 189    Renal         RXF-393_erlotinib_0nM_24h erlotinib     0nM    24h
## 190      CNS          SF-268_erlotinib_0nM_24h erlotinib     0nM    24h
## 191      CNS          SF-268_erlotinib_0nM_24h erlotinib     0nM    24h
## 192      CNS          SF-295_erlotinib_0nM_24h erlotinib     0nM    24h
## 193      CNS          SF-295_erlotinib_0nM_24h erlotinib     0nM    24h
## 194      CNS          SF-539_erlotinib_0nM_24h erlotinib     0nM    24h
## 195      CNS          SF-539_erlotinib_0nM_24h erlotinib     0nM    24h
## 196 Melanoma        SK-MEL-2_erlotinib_0nM_24h erlotinib     0nM    24h
## 197 Melanoma        SK-MEL-2_erlotinib_0nM_24h erlotinib     0nM    24h
## 198 Melanoma       SK-MEL-28_erlotinib_0nM_24h erlotinib     0nM    24h
## 199 Melanoma       SK-MEL-28_erlotinib_0nM_24h erlotinib     0nM    24h
## 200 Melanoma        SK-MEL-5_erlotinib_0nM_24h erlotinib     0nM    24h
## 201 Melanoma        SK-MEL-5_erlotinib_0nM_24h erlotinib     0nM    24h
## 202  Ovarian         SK-OV-3_erlotinib_0nM_24h erlotinib     0nM    24h
## 203  Ovarian         SK-OV-3_erlotinib_0nM_24h erlotinib     0nM    24h
## 204    Renal           SN12C_erlotinib_0nM_24h erlotinib     0nM    24h
## 205    Renal           SN12C_erlotinib_0nM_24h erlotinib     0nM    24h
## 206      CNS          SNB-19_erlotinib_0nM_24h erlotinib     0nM    24h
## 207      CNS          SNB-19_erlotinib_0nM_24h erlotinib     0nM    24h
## 208      CNS          SNB-75_erlotinib_0nM_24h erlotinib     0nM    24h
## 209      CNS          SNB-75_erlotinib_0nM_24h erlotinib     0nM    24h
## 210     <NA>              SR_erlotinib_0nM_24h erlotinib     0nM    24h
## 211    Colon          SW-620_erlotinib_0nM_24h erlotinib     0nM    24h
## 212    Colon          SW-620_erlotinib_0nM_24h erlotinib     0nM    24h
## 213   Breast           T-47D_erlotinib_0nM_24h erlotinib     0nM    24h
## 214   Breast           T-47D_erlotinib_0nM_24h erlotinib     0nM    24h
## 215    Renal           TK-10_erlotinib_0nM_24h erlotinib     0nM    24h
## 216    Renal           TK-10_erlotinib_0nM_24h erlotinib     0nM    24h
## 217      CNS            U251_erlotinib_0nM_24h erlotinib     0nM    24h
## 218      CNS            U251_erlotinib_0nM_24h erlotinib     0nM    24h
## 219 Melanoma        UACC-257_erlotinib_0nM_24h erlotinib     0nM    24h
## 220 Melanoma        UACC-257_erlotinib_0nM_24h erlotinib     0nM    24h
## 221 Melanoma         UACC-62_erlotinib_0nM_24h erlotinib     0nM    24h
## 222 Melanoma         UACC-62_erlotinib_0nM_24h erlotinib     0nM    24h
## 223    Renal           UO-31_erlotinib_0nM_24h erlotinib     0nM    24h
## 224    Renal           UO-31_erlotinib_0nM_24h erlotinib     0nM    24h
##     tissue.y
## 1      Renal
## 2      Renal
## 3      Renal
## 4      Renal
## 5       Lung
## 6       Lung
## 7      Renal
## 8      Renal
## 9     Breast
## 10    Breast
## 11     Renal
## 12     Renal
## 13  Leukemia
## 14  Prostate
## 15  Prostate
## 16      Lung
## 17      Lung
## 18     Colon
## 19     Colon
## 20     Colon
## 21     Colon
## 22     Colon
## 23     Colon
## 24  Leukemia
## 25      Lung
## 26      Lung
## 27      Lung
## 28      Lung
## 29    Breast
## 30    Breast
## 31     Colon
## 32   Ovarian
## 33   Ovarian
## 34  Leukemia
## 35     Colon
## 36     Colon
## 37  Melanoma
## 38  Melanoma
## 39  Melanoma
## 40  Melanoma
## 41  Melanoma
## 42    Breast
## 43    Breast
## 44    Breast
## 45    Breast
## 46  Melanoma
## 47  Melanoma
## 48    Breast
## 49    Breast
## 50  Leukemia
## 51  Leukemia
## 52   Ovarian
## 53   Ovarian
## 54      Lung
## 55      Lung
## 56      Lung
## 57      Lung
## 58      Lung
## 59      Lung
## 60      Lung
## 61      Lung
## 62      Lung
## 63      Lung
## 64   Ovarian
## 65   Ovarian
## 66   Ovarian
## 67   Ovarian
## 68   Ovarian
## 69   Ovarian
## 70   Ovarian
## 71   Ovarian
## 72  Prostate
## 73  Prostate
## 74  Leukemia
## 75  Leukemia
## 76     Renal
## 77     Renal
## 78       CNS
## 79       CNS
## 80       CNS
## 81       CNS
## 82       CNS
## 83       CNS
## 84  Melanoma
## 85  Melanoma
## 86  Melanoma
## 87  Melanoma
## 88  Melanoma
## 89  Melanoma
## 90   Ovarian
## 91   Ovarian
## 92     Renal
## 93     Renal
## 94       CNS
## 95       CNS
## 96       CNS
## 97       CNS
## 98  Leukemia
## 99     Colon
## 100    Colon
## 101   Breast
## 102   Breast
## 103    Renal
## 104    Renal
## 105      CNS
## 106      CNS
## 107 Melanoma
## 108 Melanoma
## 109 Melanoma
## 110 Melanoma
## 111    Renal
## 112    Renal
## 113    Renal
## 114    Renal
## 115    Renal
## 116    Renal
## 117     Lung
## 118     Lung
## 119    Renal
## 120    Renal
## 121   Breast
## 122   Breast
## 123    Renal
## 124    Renal
## 125 Leukemia
## 126 Prostate
## 127 Prostate
## 128     Lung
## 129     Lung
## 130    Colon
## 131    Colon
## 132    Colon
## 133    Colon
## 134    Colon
## 135    Colon
## 136 Leukemia
## 137     Lung
## 138     Lung
## 139     Lung
## 140     Lung
## 141   Breast
## 142   Breast
## 143    Colon
## 144  Ovarian
## 145  Ovarian
## 146 Leukemia
## 147    Colon
## 148    Colon
## 149 Melanoma
## 150 Melanoma
## 151 Melanoma
## 152 Melanoma
## 153 Melanoma
## 154   Breast
## 155   Breast
## 156   Breast
## 157   Breast
## 158 Melanoma
## 159 Melanoma
## 160   Breast
## 161   Breast
## 162 Leukemia
## 163 Leukemia
## 164  Ovarian
## 165  Ovarian
## 166     Lung
## 167     Lung
## 168     Lung
## 169     Lung
## 170     Lung
## 171     Lung
## 172     Lung
## 173     Lung
## 174     Lung
## 175     Lung
## 176  Ovarian
## 177  Ovarian
## 178  Ovarian
## 179  Ovarian
## 180  Ovarian
## 181  Ovarian
## 182  Ovarian
## 183  Ovarian
## 184 Prostate
## 185 Prostate
## 186 Leukemia
## 187 Leukemia
## 188    Renal
## 189    Renal
## 190      CNS
## 191      CNS
## 192      CNS
## 193      CNS
## 194      CNS
## 195      CNS
## 196 Melanoma
## 197 Melanoma
## 198 Melanoma
## 199 Melanoma
## 200 Melanoma
## 201 Melanoma
## 202  Ovarian
## 203  Ovarian
## 204    Renal
## 205    Renal
## 206      CNS
## 207      CNS
## 208      CNS
## 209      CNS
## 210 Leukemia
## 211    Colon
## 212    Colon
## 213   Breast
## 214   Breast
## 215    Renal
## 216    Renal
## 217      CNS
## 218      CNS
## 219 Melanoma
## 220 Melanoma
## 221 Melanoma
## 222 Melanoma
## 223    Renal
## 224    Renal
rmv.rows = apply(el, 1, function(x) {
  sum(is.na(x))
})  # Go through each row and sum up all missing values
row.names(rmv.rows)

Create data frame with lapatinib and erlotinib data

fc<-(Treated-Untreated)
fc<-data.frame(scale(fc))
all<-data.frame(fc[grep("lapatinib|erlotinib", colnames(fc))])

since erlotinip contains more columns than lapatinib, we have to remove these columns

all.rmv<-all[, -which(colnames(all) %in% c(
                    "CCRF.CEM_erlotinib_0nM_24h", 
                    "HL.60_erlotinib_0nM_24h", 
                    "HT29_erlotinib_0nM_24h", 
                    "K.562_erlotinib_0nM_24h", 
                    "LOX_erlotinib_0nM_24h",
                    "SR_erlotinib_0nM_24h",
                    "COLO.205_lapatinib_0nM_24h"))]

Checking the rows

la<-data.frame(all.rmv[grep("lapatinib", colnames(all.rmv))])
ncol(la)
## [1] 0
er<-data.frame(all.rmv[grep("erlotinib", colnames(all.rmv))])
ncol(er)
## [1] 0
erla<-data.frame(er,la)
ncol(all.rmv) #to prove if the columns are removed
## [1] 0

Anova

p = 0.2 means that the result does not differ significantly. Thus, the two drugs did not differ significantly from each other.

```{r}

drug<-c(rep(‘Erlotinib’,53), rep(‘Lapatinib’,53))

expression_drug<-apply(erla, MARGIN = 2, sum)

df_drug<-data.frame(expression_drug, drug)

library(ggpubr)

ggboxplot (data = df_drug, x=“drug”, y=“expression_drug”, color = “drug”,

# add = “jitter”, legend = “none”)+ # rotate_x_text(angle = 45)+ # geom_hline(yintercept = mean(lapatinib$MCF7_lapatinib_0nM_24h), linetype = 2)+ # Add horizontal line at base mean # stat_compare_means(method = “anova”)+ # Add global annova p-value # stat_compare_means(label = “p.signif”, method = “t.test”, # ref.group = “.all.”, hide.ns = TRUE) # Pairwise comparison against all

```

Question 3: Comparing lapatinib treated breast and cns celllines

L_fc <- select(Fold_Change, contains("Lapa"))
L_fc <- as.data.frame(t(L_fc))
rownames(Metadata) <- Metadata$sample


L_treated <- select(Treated, contains("Lapa"))
L_treated <- t(L_treated)
L_untreated <- select(Untreated, contains("Lapa"))
L_untreated <- t(L_untreated)



# selecting breast Lapatinib samples
breast <- Metadata[Metadata[,'tissue']=="Breast",]
rownames(breast) <- breast$sample
rownames(breast) <- gsub(x = rownames(breast), pattern = "-", replacement = ".")  

breastFC <- subset(L_fc, rownames(L_fc) %in% rownames(breast))
breastTreated <- subset(L_treated, rownames(L_treated) %in% rownames(breast))
breastUntreated <- subset(L_untreated, rownames(L_untreated) %in% rownames(breast))#


# selecting CNS Lapatinib samples
cns <- Metadata[Metadata[,'tissue']=="CNS",]
rownames(cns) <- cns$sample
rownames(cns) <- gsub(x = rownames(cns), pattern = "-", replacement = ".")

cnsFC <- subset(L_fc, rownames(L_fc) %in% rownames(cns))
cnsTreated <- subset(L_treated, rownames(L_treated) %in% rownames(cns))
cnsUntreated <- subset(L_untreated, rownames(L_untreated) %in% rownames(cns))
#performing a paired t-test of treated and untreated samples
t_test_cns <- col_t_paired(cnsTreated, cnsUntreated, alternative = "two.sided", mu = 0,conf.level = 0.95)
t_test_breast <- col_t_paired(breastTreated, breastUntreated, alternative = "two.sided", mu = 0,conf.level = 0.95)

#obtaining Benjamini-Hochberg adjusted p-values
pval_cns <- t_test_cns$pvalue
pval_breast <- t_test_breast$pvalue

fdr_cns <- p.adjust(pval_cns, "BH")
fdr_breast <- p.adjust(pval_breast, "BH")


#obtaining mean FC values over all samples 
breastFCm <- as.numeric(colMeans(breastFC))
cnsFCm <- as.numeric(colMeans(cnsFC))
genes <- colnames(breastFC)
## breast volcano plot
#creating a matrix containg all needed values for plotting
diff_df_breast <- data.frame(gene = genes, Fold = breastFCm, FDR = fdr_breast)
diff_df_breast$absFold <- abs(diff_df_breast$Fold)
head(diff_df_breast)
##     gene         Fold       FDR     absFold
## 1   A1CF  0.037268413 0.8765540 0.037268413
## 2    A2M -0.032213825 0.7188608 0.032213825
## 3 A4GALT  0.006012452 0.9793436 0.006012452
## 4  A4GNT -0.053969518 0.4235638 0.053969518
## 5   AAAS  0.081656784 0.5283372 0.081656784
## 6   AACS  0.023767096 0.8115022 0.023767096
# add a grouping column; default value is "not significant"
diff_df_breast$group <- "NotSignificant"



# change the grouping for the entries with significance but not a large enough Fold change
diff_df_breast[which(diff_df_breast['FDR'] < 0.5 & (diff_df_breast['absFold']) < 0.2 ),"group"] <- "Significant"

# change the grouping for the entries a large enough Fold change but not a low enough p value
diff_df_breast[which(diff_df_breast['FDR'] > 0.5 & (diff_df_breast['absFold']) > 0.2 ),"group"] <- "FoldChange"

# change the grouping for the entries with both significance and large enough fold change
diff_df_breast[which(diff_df_breast['FDR'] < 0.5 & (diff_df_breast['absFold']) > 0.2 ),"group"] <- "Significant&FoldChange"


# Find and label the top peaks.
top_peaks_breast <- diff_df_breast[with(diff_df_breast, order(Fold, FDR)),][1:10,]
top_peaks_breast <- rbind(top_peaks_breast, diff_df_breast[with(diff_df_breast, order(-Fold, FDR)),][1:10,])


# Add gene labels for all of the top genes we found
# creating an empty list, and filling it with entries for each row in the dataframe
# each list entry is another list with named items that will be used 
a <- list()
for (i in seq_len(nrow(top_peaks_breast))) {
  m <- top_peaks_breast[i, ]
  a[[i]] <- list(
    x = m[["Fold"]],
    y = -log10(m[["FDR"]]),
    text = m[["gene"]],
    xref = "x",
    yref = "y",
    showarrow = TRUE,
    arrowhead = 0.5,
    ax = 20,
    ay = -40
  )
}

plot_breast <- plot_ly(data = diff_df_breast, x = diff_df_breast$Fold, y = -log10(diff_df_breast$FDR), type = "scatter", text = diff_df_breast$gene, mode = "markers", color = diff_df_breast$group) %>% 
  layout(title ="Volcano Plot of Lapatinib breast cancer samples", 
         xaxis = list(title="log2 Fold Change"),
         yaxis = list(title="FDR")) %>%
  layout(annotations = a)
plot_breast
###thresholds still need to be discussed
## CNS volcano plot

diff_df_cns <- data.frame(gene = genes, Fold = cnsFCm, FDR = fdr_cns)
diff_df_cns$absFold <- abs(diff_df_cns$Fold)
head(diff_df_cns)
##     gene         Fold       FDR     absFold
## 1   A1CF  0.066575311 0.5939566 0.066575311
## 2    A2M  0.038348381 0.6873009 0.038348381
## 3 A4GALT  0.000390011 0.9980719 0.000390011
## 4  A4GNT -0.018219799 0.8780106 0.018219799
## 5   AAAS  0.014723327 0.9008420 0.014723327
## 6   AACS  0.003887384 0.9870209 0.003887384
# add a grouping column; default value is "not significant"
diff_df_cns$group <- "NotSignificant"


# change the grouping for the entries with significance but not a large enough Fold change
diff_df_cns[which(diff_df_cns['FDR'] < 0.5 & (diff_df_cns['absFold']) < 0.2 ),"group"] <- "Significant"

# change the grouping for the entries a large enough Fold change but not a low enough p value
diff_df_cns[which(diff_df_cns['FDR'] > 0.5 & (diff_df_cns['absFold']) > 0.2 ),"group"] <- "FoldChange"

# change the grouping for the entries with both significance and large enough fold change
diff_df_cns[which(diff_df_cns['FDR'] < 0.5 & (diff_df_cns['absFold']) > 0.2 ),"group"] <- "Significant&FoldChange"


# Find and label the top peaks..
top_peaks_cns <- diff_df_cns[with(diff_df_cns, order(Fold, FDR)),][1:10,]
top_peaks_cns <- rbind(top_peaks_cns, diff_df_cns[with(diff_df_cns, order(-Fold, FDR)),][1:10,])


a <- list()
for (i in seq_len(nrow(top_peaks_cns))) {
  m <- top_peaks_cns[i, ]
  a[[i]] <- list(
    x = m[["Fold"]],
    y = -log10(m[["FDR"]]),
    text = m[["gene"]],
    xref = "x",
    yref = "y",
    showarrow = TRUE,
    arrowhead = 0.5,
    ax = 20,
    ay = -40
  )
}

plot_cns <- plot_ly(data = diff_df_cns, x = diff_df_cns$Fold, y = -log10(diff_df_cns$FDR),type = "scatter", text = diff_df_cns$gene, mode = "markers", color = diff_df_cns$group) %>% 
  layout(title ="Volcano Plot of Lapatinib CNS cancer samples",
         xaxis = list(title="log2 Fold Change"),
         yaxis = list(title="FDR"))%>%
  layout(annotations = a)
plot_cns
# selecet top peak genes common in cns and breast tissue
tpb_comparison <- subset(top_peaks_breast, gene %in% top_peaks_cns$gene)
tpc_comparison <- subset(top_peaks_cns, gene %in% top_peaks_breast$gene)


# order common genes alphabetically
tpb_comparison <- tpb_comparison[order(tpb_comparison$gene),]
tpc_comparison <- tpc_comparison[order(tpc_comparison$gene),]


## creating heat map of FCs to compare values 
cor_mat <- cbind("breast" = tpb_comparison$Fold, "cns" = tpc_comparison$Fold)
rownames(cor_mat) <- tpb_comparison$gene
data <- read.delim


pheatmap(
  mat               = cor_mat,
  color             = magma(10),
  border_color      = "black",
  show_colnames     = TRUE,
  show_rownames     = TRUE,
  drop_levels       = TRUE,
  fontsize          = 14,
  main              = "Comparison:
  FC levels of cns and breast top peak genes"
)

======= still in progress >>>>>>> cd0d16c457bb9d18b398e24c369d80b672402313